Sources of carbon supporting the fast growth of developing immature moso bamboo (Phyllostachys edulis) culms: inference from carbon isotopes and anatomy.

Phyllostachys edulis is a spectacularly fast-growing species that completes its height growth within 2 months after the shoot emerges without producing leaves (fast-growing period, FGP). This phase was considered heterotrophic, with the carbon necessary for the growth being transferred from the mature culms via the rhizomes, although previous studies observed key enzymes and anatomical features related to C4-carbon fixation in developing culms. We tested whether C4-photosynthesis or dark-CO2 fixation through anaplerotic reactions significantly contributes to the FGP, resulting in differences in the natural abundance of δ13C in bulk organic matter and organic compounds. Further, pulse-13CO2-labelling was performed on developing culms, either from the surface or from the internal hollow, to ascertain whether significant CO2 fixation occurs in developing culms. δ13C of young shoots and developing culms were higher (-26.3 to -26.9 ‰) compared to all organs of mature bamboos (-28.4 to -30.1 ‰). Developing culms contained chlorophylls, most observed in the skin tissues. After pulse-13CO2-labelling, the polar fraction extracted from the skin tissues was slightly enriched in 13C, and only a weak 13C enrichment was observed in inner tissues. Main carbon source sustaining the FGP was not assimilated by the developing culm, while a limited anaplerotic fixation of respired CO2 cannot be excluded and is more likely than C4-photosynthetic carbon fixation.

Estimating divergent forest carbon stocks and sinks via a knife set approach.

Forest carbon stocks and sinks (CSSs) have been widely estimated using climate classification tables and linear regression (LR) models with common independent variables (IVs) such as the average diameter at breast height (DBH) of stems and root shoot ratio. However, this approach is relatively ineffective when the explanatory power of IVs is lower than that of unobservable variables. Various environmental and anthropogenic factors affect target variables that cause the correlation between them to be chaotic. Here, we designed a knife set (KS) approach combining LR models and the wandering through random forests (WTF) algorithm and applied it in a specific case of Phyllostachys edulis (Carrière) J. Houz. (P. edulis) forests, which have an irregular relationship between their belowground carbon (BGC) stocks and average DBH. We then validated the KS approach performed by cluster computing to estimate the aboveground carbon (AGC) and BGC stocks and the total net primary production (TNPP). The estimated CSSs were compared to the benchmark of the methodology that applied Tier 1 in the Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories via 10-fold cross validation, and the KS approach significantly increased precision and accuracy of estimations. Our approach provides general insights to accurately estimate forest CSSs relying on evidence-based field data, even if some target variables are divergent in specific forest types. We also pointed out the reason why current fancy models containing machine learning (ML) or deep learning algorithms are not effective in predicting the target variables of certain chaotic systems is perhaps that the total explanatory power of observable variables is less than that of the total unobservable variables. Quantifying unobservable variables into observable variables is a linchpin of future works related to chaotic system estimation.

Accessing the nursing behaviour of Moso bamboo (Phyllostachys edilus) on carbohydrates dynamics and photosystems.

Nursing behaviour, also known as breastfeeding behaviour, is the feeding of juvenile individuals with nutrients or proteins from matures especially in mammals. As a hypothetical phenomenon in bamboo forests, mature bamboos have transferred photoassimilates to young bamboos for recovering and rebuilding their photosystems especially in winter. This process is accompanied by changes in the ability of photosystems and the mass fraction of non-structural carbohydrates (NSCs), structural carbohydrates (SCs), and lignin. We analysed carbohydrates and chlorophyll fluorescence to compare the physiological traits in mature (age 2, 3, 4) and immature (age 1) Moso bamboos (Phyllostachys edilus) during a year using the Portable Chlorophyll Fluorometer (PCF) and the Liquid Chromatographic (LC) method. The results showed that the mass fraction of total soluble carbohydrates (TSCs) and starch in the bottom of bamboo at age 1 was higher than other parts and ages in spring, whereas the mass fraction of TSCs, starch, and sucrose at age 3 was higher than other parts and ages in winter. The Fv/Fm, an indicator to reveal photosystems were functional or not, at age 1 dramatically dropped when the cold current attacked first time in October, and then quickly recovered in November. Our findings indicate that mature bamboos very possibly provide carbohydrates to immature bamboos and help them rebuild their photosystems when a bamboo forest resists cold stress.